Instructions to use zeromodels/depth_anything_v2_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasFormers
How to use zeromodels/depth_anything_v2_base with KerasFormers:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Keras
How to use zeromodels/depth_anything_v2_base with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://zeromodels/depth_anything_v2_base") - DepthAnythingV2
How to use zeromodels/depth_anything_v2_base with DepthAnythingV2:
# Install from https://github.com/DepthAnything/Depth-Anything-V2 # Load the model and infer depth from an image import cv2 import torch from depth_anything_v2.dpt import DepthAnythingV2 # instantiate the model model = DepthAnythingV2(encoder="<ENCODER>", features=<NUMBER_OF_FEATURES>, out_channels=<OUT_CHANNELS>) # load the weights filepath = hf_hub_download(repo_id="zeromodels/depth_anything_v2_base", filename="depth_anything_v2_<ENCODER>.pth", repo_type="model") state_dict = torch.load(filepath, map_location="cpu") model.load_state_dict(state_dict).eval() raw_img = cv2.imread("your/image/path") depth = model.infer_image(raw_img) # HxW raw depth map in numpy - Notebooks
- Google Colab
- Kaggle
File size: 811 Bytes
874439b f1c3f0a 874439b f1c3f0a 6b3abd8 874439b f1c3f0a 874439b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | {
"library_name": "kerasformers",
"kerasformers_version": "1.2.1",
"model_module": "kerasformers.models.depth_anything_v2",
"model_class": "DepthAnythingV2DepthEstimation",
"variant": "depth_anything_v2_base",
"weights": "model.weights.h5",
"schema_version": 2,
"weight_dtype": "float32",
"model_type": "depth_anything",
"vision_config": {
"backbone_dim": 768,
"backbone_depth": 12,
"backbone_num_heads": 12,
"out_indices": [
3,
6,
9,
12
],
"neck_hidden_sizes": [
96,
192,
384,
768
],
"fusion_hidden_size": 128,
"reassemble_factors": [
4,
2,
1,
0.5
],
"depth_estimation_type": "relative",
"max_depth": 1.0,
"image_size": 518
}
} |